library(tidyverse)
library(ggplot2)
library(lavaan)
library(car)
library(glmnet)
library(randomForestSRC)
library(caret)
library(ggRandomForests)
library(VSURF)VSURF
Data set
This data set is from the 2015 Asian American Quality of Life survey. Participants are from Austin, Texas.
Input data set
qol <- read_csv("AAQoL.csv") |> mutate(across(where(is.character), ~as.factor(.x))) |>
mutate(`English Difficulties`=relevel(`English Difficulties`,ref="Not at all"),
`English Speaking`=relevel(`English Speaking`,ref="Not at all"),
Ethnicity = relevel(Ethnicity,ref="Chinese"),
Religion=relevel(Religion,ref="None")) |>
mutate(Income_median = case_match(Income,"$0 - $9,999"~"Below",
"$10,000 - $19,999" ~"Below",
"$20,000 - $29,999"~"Below",
"$30,000 - $39,999"~"Below",
"$40,000 - $49,999"~"Below",
"$50,000 - $59,999"~"Below",
"$60,000 - $69,999"~"Above",
"$70,000 and over"~"Above",
.default=Income)) |>
mutate(Income_median = factor(Income_median, levels=c("Below","Above"))) |>
mutate(across(c(`Family Respect`:`Togetherness`,`Close-knit Community`:`Community Trust`),~relevel(.x,ref="Strongly disagree")))|>
mutate(across(c(`See Family`:`Helpful Friends`,`Discrimination`),~as.factor(.x)))New names:
Rows: 2609 Columns: 231
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(190): Gender, Ethnicity, Marital Status, No One, Spouse, Children, Gran... dbl
(41): Survey ID, Age, Education Completed, Household Size, Grandparent,...
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `Other` -> `Other...17`
• `Other` -> `Other...89`qol |> DT::datatable()Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.htmlFamily
rfdata <- qol |> filter(Family %in% c("No","Yes")) |>
mutate(Family=droplevels(Family)) |>
select(Family, Ethnicity, Age, Gender,Religion, `Full Time Employment`, Income_median, `English Speaking`, `English Difficulties`,`See Family`:`Community Trust`,`Health Insurance`,`Dental Insurance`,`Discrimination`) |>
na.omit() |>
as.data.frame() |>
rename_with(make.names)
imbal <- ROSE::ROSE(Family~.,
data=rfdata,
seed=3)$data
VSURF(Family~.,imbal,na.action="na.omit",parallel=T,verbose=F)->vsurf.modWarning in VSURF.formula(Family ~ ., imbal, na.action = "na.omit", parallel = T, : VSURF with a formula-type call outputs selected variables
which are indices of the input matrix based on the formula:
you may reorder these to get indices of the original datavsurf.mod |> summary()
VSURF computation time: 1 mins
VSURF selected:
34 variables at thresholding step (in 8.5 secs)
29 variables at interpretation step (in 7.3 secs)
14 variables at prediction step (in 46.6 secs)
VSURF ran in parallel on a PSOCK cluster and used 15 cores names(rfdata[,-1])[vsurf.mod$varselect.pred] [1] "Ethnicity" "Age" "See.Friends"
[4] "Close.Family" "English.Difficulties" "See.Family"
[7] "Religion" "Helpful.Family" "Helpful.Friends"
[10] "Helpful.Community" "Full.Time.Employment" "Community.Trust"
[13] "English.Speaking" "Successful.Family" names(rfdata[,-1])[vsurf.mod$varselect.interp] [1] "Ethnicity" "Age"
[3] "See.Friends" "Close.Family"
[5] "English.Difficulties" "See.Family"
[7] "Religion" "Helpful.Family"
[9] "Helpful.Friends" "Helpful.Community"
[11] "Close.Friends" "Full.Time.Employment"
[13] "Community.Trust" "Get.Along"
[15] "Religious.Importance" "English.Speaking"
[17] "Community.Shares.Values" "Expression"
[19] "Religious.Attendance" "Close.knit.Community"
[21] "Family.Respect" "Successful.Family"
[23] "Discrimination" "Feel.Close"
[25] "Similar.Values" "Spend.Time.Together"
[27] "Togetherness" "Trust"
[29] "Loyalty" plot(vsurf.mod)
vsurf.mod$mean.perf[1] 0.1639408Importance
vi<- data.frame(Variable=names(rfdata[,-1])[vsurf.mod$imp.mean.dec.ind],
Importance = vsurf.mod$imp.mean.dec,
sd_Importance = vsurf.mod$imp.sd.dec
)|>
mutate(fill = case_when(Variable=="Ethnicity"~"red",
.default="black"))
vi |> mutate(across(Importance:sd_Importance,~round(.x,5))) Variable Importance sd_Importance fill
1 Ethnicity 0.06500 0.00138 red
2 Age 0.05911 0.00097 black
3 See.Friends 0.04469 0.00107 black
4 Close.Family 0.03984 0.00089 black
5 English.Difficulties 0.03830 0.00091 black
6 See.Family 0.03650 0.00090 black
7 Religion 0.03581 0.00054 black
8 Helpful.Family 0.03328 0.00070 black
9 Helpful.Friends 0.03266 0.00065 black
10 Helpful.Community 0.03202 0.00114 black
11 Close.Friends 0.02930 0.00075 black
12 Full.Time.Employment 0.02876 0.00130 black
13 Community.Trust 0.02418 0.00092 black
14 Get.Along 0.02123 0.00086 black
15 Religious.Importance 0.02044 0.00089 black
16 English.Speaking 0.02008 0.00063 black
17 Community.Shares.Values 0.01954 0.00072 black
18 Expression 0.01950 0.00090 black
19 Religious.Attendance 0.01943 0.00082 black
20 Close.knit.Community 0.01633 0.00052 black
21 Family.Respect 0.01632 0.00096 black
22 Successful.Family 0.01439 0.00062 black
23 Discrimination 0.01343 0.00081 black
24 Feel.Close 0.01168 0.00046 black
25 Similar.Values 0.01069 0.00061 black
26 Spend.Time.Together 0.01060 0.00058 black
27 Togetherness 0.01015 0.00062 black
28 Trust 0.00993 0.00056 black
29 Loyalty 0.00966 0.00052 black
30 Gender 0.00923 0.00060 black
31 Dental.Insurance 0.00800 0.00042 black
32 Family.Pride 0.00726 0.00037 black
33 Income_median 0.00635 0.00029 black
34 Health.Insurance 0.00355 0.00022 blackimportance_plot <- ggplot(vi, aes(x = reorder(Variable, Importance), y = Importance, fill=fill))+
geom_bar(stat = "identity",alpha=0.4) +
geom_errorbar(aes(ymin=Importance-sd_Importance, ymax = Importance+sd_Importance))+
labs(title = "Variable Importance", x = "Variable", y = "Importance") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
scale_fill_manual(values=c("black","red"),
guide="none")
plot(importance_plot)
ggsave(filename = "VSURF_importance_family.png", width=8, height=12,units="in")Logistic regression (Interpretation)
lr <- rfdata |> select(Family,names(rfdata[,-1])[vsurf.mod$varselect.pred])
lr_mod <- glm(Family~.,family=binomial,data=lr)
summary(lr_mod)
Call:
glm(formula = Family ~ ., family = binomial, data = lr)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.151943 0.639958 -0.237 0.812326
EthnicityAsian Indian -0.192905 0.296161 -0.651 0.514820
EthnicityFilipino -0.446187 0.225409 -1.979 0.047765
EthnicityKorean -0.741733 0.169726 -4.370 1.24e-05
EthnicityOther -0.859275 0.249074 -3.450 0.000561
EthnicityVietnamese -0.746836 0.183016 -4.081 4.49e-05
Age -0.015818 0.003318 -4.767 1.87e-06
See.Friends1 -0.853346 0.336875 -2.533 0.011305
See.Friends2 -0.843787 0.313717 -2.690 0.007153
See.Friends3 -0.703854 0.306124 -2.299 0.021491
See.Friends4 -0.570473 0.321137 -1.776 0.075664
See.Friends5 -0.431305 0.329648 -1.308 0.190745
Close.Family1 0.587378 0.242732 2.420 0.015526
Close.Family2 0.329151 0.235289 1.399 0.161836
Close.Family3 0.485595 0.239437 2.028 0.042553
Close.Family4 0.427955 0.288749 1.482 0.138313
Close.Family5 0.348586 0.342392 1.018 0.308634
English.DifficultiesMuch 0.540725 0.163427 3.309 0.000937
English.DifficultiesNot much 0.145775 0.151769 0.961 0.336798
English.DifficultiesVery much -0.192473 0.146085 -1.318 0.187658
See.Family1 0.385263 0.307594 1.253 0.210386
See.Family2 0.361555 0.261022 1.385 0.166006
See.Family3 0.251662 0.242713 1.037 0.299796
See.Family4 0.371602 0.259331 1.433 0.151879
See.Family5 0.129077 0.276714 0.466 0.640882
ReligionBuddhist 0.212525 0.192510 1.104 0.269606
ReligionCatholic 0.188857 0.188028 1.004 0.315182
ReligionHindu -0.223376 0.307336 -0.727 0.467340
ReligionMuslim -0.092396 0.381967 -0.242 0.808862
ReligionOther -0.253753 0.402710 -0.630 0.528621
ReligionProtestant 0.053290 0.158890 0.335 0.737329
Helpful.Family1 0.184640 0.334258 0.552 0.580683
Helpful.Family2 0.500517 0.322085 1.554 0.120187
Helpful.Family3 0.602626 0.326501 1.846 0.064934
Helpful.Family4 0.917988 0.355481 2.582 0.009812
Helpful.Family5 0.826266 0.392080 2.107 0.035084
Helpful.Friends1 -0.075096 0.279876 -0.268 0.788453
Helpful.Friends2 -0.079572 0.265097 -0.300 0.764054
Helpful.Friends3 -0.287158 0.269523 -1.065 0.286682
Helpful.Friends4 -0.200507 0.303249 -0.661 0.508487
Helpful.Friends5 -0.214294 0.323670 -0.662 0.507922
Helpful.CommunityAgree 0.823976 0.455258 1.810 0.070310
Helpful.CommunityDisagree 1.163599 0.463733 2.509 0.012100
Helpful.CommunityNeutral 0.807602 0.451168 1.790 0.073450
Helpful.CommunityStrongly agree 0.883474 0.491665 1.797 0.072351
Full.Time.EmploymentEmployed full time -0.410523 0.101625 -4.040 5.35e-05
Community.TrustAgree -0.006419 0.386045 -0.017 0.986733
Community.TrustDisagree -0.008605 0.382796 -0.022 0.982065
Community.TrustNeutral -0.126423 0.375786 -0.336 0.736552
Community.TrustStrongly agree 0.061553 0.441427 0.139 0.889101
English.SpeakingNot well -0.171237 0.252077 -0.679 0.496945
English.SpeakingVery well -0.007144 0.262718 -0.027 0.978305
English.SpeakingWell -0.210886 0.249962 -0.844 0.398854
Successful.FamilySomewhat agree 0.131823 0.303391 0.435 0.663925
Successful.FamilySomewhat disagree 0.291325 0.336694 0.865 0.386901
Successful.FamilyStrongly agree 0.279404 0.303984 0.919 0.358022
(Intercept)
EthnicityAsian Indian
EthnicityFilipino *
EthnicityKorean ***
EthnicityOther ***
EthnicityVietnamese ***
Age ***
See.Friends1 *
See.Friends2 **
See.Friends3 *
See.Friends4 .
See.Friends5
Close.Family1 *
Close.Family2
Close.Family3 *
Close.Family4
Close.Family5
English.DifficultiesMuch ***
English.DifficultiesNot much
English.DifficultiesVery much
See.Family1
See.Family2
See.Family3
See.Family4
See.Family5
ReligionBuddhist
ReligionCatholic
ReligionHindu
ReligionMuslim
ReligionOther
ReligionProtestant
Helpful.Family1
Helpful.Family2
Helpful.Family3 .
Helpful.Family4 **
Helpful.Family5 *
Helpful.Friends1
Helpful.Friends2
Helpful.Friends3
Helpful.Friends4
Helpful.Friends5
Helpful.CommunityAgree .
Helpful.CommunityDisagree *
Helpful.CommunityNeutral .
Helpful.CommunityStrongly agree .
Full.Time.EmploymentEmployed full time ***
Community.TrustAgree
Community.TrustDisagree
Community.TrustNeutral
Community.TrustStrongly agree
English.SpeakingNot well
English.SpeakingVery well
English.SpeakingWell
Successful.FamilySomewhat agree
Successful.FamilySomewhat disagree
Successful.FamilyStrongly agree
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2667.5 on 1925 degrees of freedom
Residual deviance: 2494.4 on 1870 degrees of freedom
AIC: 2606.4
Number of Fisher Scoring iterations: 4car::Anova(lr_mod)Analysis of Deviance Table (Type II tests)
Response: Family
LR Chisq Df Pr(>Chisq)
Ethnicity 30.9275 5 9.681e-06 ***
Age 22.9842 1 1.633e-06 ***
See.Friends 11.6087 5 0.0405619 *
Close.Family 7.3520 5 0.1957467
English.Difficulties 20.2105 3 0.0001535 ***
See.Family 4.2583 5 0.5128586
Religion 3.5836 6 0.7328123
Helpful.Family 10.7345 5 0.0569065 .
Helpful.Friends 2.7763 5 0.7344305
Helpful.Community 7.5326 4 0.1102821
Full.Time.Employment 16.4111 1 5.098e-05 ***
Community.Trust 1.2493 4 0.8699092
English.Speaking 2.6321 3 0.4518928
Successful.Family 2.6171 3 0.4544932
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1broom::tidy(lr_mod,exponentiate=T,conf.int=T) |> DT::datatable()Health Professionals
rfdata <- qol |> select(`Heal Professionals`, Ethnicity, Age, Gender,Religion, `Full Time Employment`, Income_median, `English Speaking`, `English Difficulties`,`See Family`:`Community Trust`,`Health Insurance`,`Dental Insurance`,`Discrimination`) |>
na.omit() |>
as.data.frame() |>
rename_with(make.names)
imbal <- ROSE::ROSE(Heal.Professionals~.,
data=rfdata,
seed=3)$data
VSURF(Heal.Professionals~.,imbal,na.action="na.omit",parallel=T,verbose=F)->vsurf.modWarning in VSURF.formula(Heal.Professionals ~ ., imbal, na.action = "na.omit", : VSURF with a formula-type call outputs selected variables
which are indices of the input matrix based on the formula:
you may reorder these to get indices of the original datavsurf.mod |> summary()
VSURF computation time: 32.6 secs
VSURF selected:
34 variables at thresholding step (in 8.6 secs)
15 variables at interpretation step (in 6.7 secs)
13 variables at prediction step (in 17.2 secs)
VSURF ran in parallel on a PSOCK cluster and used 15 cores names(rfdata[,-1])[vsurf.mod$varselect.pred] [1] "English.Speaking" "Dental.Insurance" "Religion"
[4] "English.Difficulties" "See.Family" "Helpful.Friends"
[7] "Close.Family" "See.Friends" "Helpful.Family"
[10] "Close.Friends" "Community.Trust" "Get.Along"
[13] "Health.Insurance" names(rfdata[,-1])[vsurf.mod$varselect.interp] [1] "English.Speaking" "Ethnicity" "Dental.Insurance"
[4] "Religion" "English.Difficulties" "See.Family"
[7] "Helpful.Friends" "Close.Family" "See.Friends"
[10] "Helpful.Family" "Close.Friends" "Community.Trust"
[13] "Religious.Attendance" "Get.Along" "Health.Insurance" plot(vsurf.mod)
vsurf.mod$mean.perf[1] 0.1527504Importance
vi<- data.frame(Variable=names(rfdata[,-1])[vsurf.mod$imp.mean.dec.ind],
Importance = vsurf.mod$imp.mean.dec,
sd_Importance = vsurf.mod$imp.sd.dec
)|>
mutate(fill = case_when(Variable=="Ethnicity"~"red",
.default="black"))
vi |> mutate(across(Importance:sd_Importance,~round(.x,5))) Variable Importance sd_Importance fill
1 English.Speaking 0.06230 0.00164 black
2 Ethnicity 0.05856 0.00108 red
3 Dental.Insurance 0.05608 0.00154 black
4 Religion 0.05139 0.00092 black
5 English.Difficulties 0.04493 0.00118 black
6 See.Family 0.04208 0.00069 black
7 Helpful.Friends 0.03669 0.00063 black
8 Close.Family 0.03392 0.00066 black
9 See.Friends 0.03375 0.00073 black
10 Helpful.Family 0.03335 0.00064 black
11 Close.Friends 0.03258 0.00072 black
12 Community.Trust 0.02832 0.00089 black
13 Religious.Attendance 0.02703 0.00076 black
14 Get.Along 0.02444 0.00072 black
15 Health.Insurance 0.02439 0.00085 black
16 Age 0.02324 0.00068 black
17 Helpful.Community 0.02284 0.00082 black
18 Close.knit.Community 0.02095 0.00071 black
19 Community.Shares.Values 0.01993 0.00078 black
20 Income_median 0.01901 0.00089 black
21 Religious.Importance 0.01819 0.00061 black
22 Spend.Time.Together 0.01746 0.00066 black
23 Successful.Family 0.01062 0.00051 black
24 Expression 0.01060 0.00044 black
25 Family.Respect 0.01034 0.00055 black
26 Feel.Close 0.00951 0.00054 black
27 Family.Pride 0.00923 0.00045 black
28 Similar.Values 0.00919 0.00034 black
29 Loyalty 0.00844 0.00049 black
30 Trust 0.00835 0.00039 black
31 Gender 0.00811 0.00044 black
32 Full.Time.Employment 0.00740 0.00049 black
33 Togetherness 0.00727 0.00033 black
34 Discrimination 0.00589 0.00036 blackimportance_plot <- ggplot(vi, aes(x = reorder(Variable, Importance), y = Importance, fill=fill))+
geom_bar(stat = "identity",alpha=0.4) +
geom_errorbar(aes(ymin=Importance-sd_Importance, ymax = Importance+sd_Importance))+
labs(title = "Variable Importance", x = "Variable", y = "Importance") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
scale_fill_manual(values=c("black","red"),
guide="none")
plot(importance_plot)
ggsave(filename = "VSURF_importance_hp.png", width=12, height=8,units="in")Logistic regression (Interpretation)
lr <- rfdata |> select(Heal.Professionals,names(rfdata[,-1])[vsurf.mod$varselect.pred])
lr_mod <- glm(Heal.Professionals~.,family=binomial,data=lr)
summary(lr_mod)
Call:
glm(formula = Heal.Professionals ~ ., family = binomial, data = lr)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.300882 0.569952 -4.037 5.41e-05 ***
English.SpeakingNot well 0.251551 0.264881 0.950 0.342277
English.SpeakingVery well 1.099407 0.266594 4.124 3.73e-05 ***
English.SpeakingWell 0.845659 0.258054 3.277 0.001049 **
Dental.InsuranceYes 0.312464 0.114703 2.724 0.006447 **
ReligionBuddhist 0.467136 0.178422 2.618 0.008841 **
ReligionCatholic 0.349362 0.163092 2.142 0.032185 *
ReligionHindu -0.468336 0.168810 -2.774 0.005531 **
ReligionMuslim 0.267734 0.332710 0.805 0.420988
ReligionOther 0.422495 0.414152 1.020 0.307660
ReligionProtestant 0.091709 0.147868 0.620 0.535121
English.DifficultiesMuch -0.442586 0.164929 -2.683 0.007286 **
English.DifficultiesNot much -0.302170 0.153173 -1.973 0.048526 *
English.DifficultiesVery much -0.305888 0.147497 -2.074 0.038092 *
See.Family1 0.266631 0.309559 0.861 0.389060
See.Family2 0.397140 0.265486 1.496 0.134681
See.Family3 0.363831 0.246453 1.476 0.139872
See.Family4 0.506602 0.263155 1.925 0.054216 .
See.Family5 0.380649 0.280586 1.357 0.174901
Helpful.Friends1 -0.495859 0.311957 -1.590 0.111946
Helpful.Friends2 -0.241957 0.297771 -0.813 0.416470
Helpful.Friends3 -0.120127 0.307556 -0.391 0.696103
Helpful.Friends4 -0.113578 0.350369 -0.324 0.745811
Helpful.Friends5 0.006169 0.386592 0.016 0.987267
Close.Family1 -0.012196 0.242751 -0.050 0.959930
Close.Family2 0.051024 0.234865 0.217 0.828015
Close.Family3 -0.031272 0.239950 -0.130 0.896306
Close.Family4 0.070974 0.291916 0.243 0.807904
Close.Family5 0.057326 0.353754 0.162 0.871267
See.Friends1 0.373373 0.356221 1.048 0.294569
See.Friends2 0.350593 0.340794 1.029 0.303595
See.Friends3 0.587777 0.331973 1.771 0.076635 .
See.Friends4 0.602412 0.345953 1.741 0.081629 .
See.Friends5 0.801026 0.352119 2.275 0.022914 *
Helpful.Family1 -0.068213 0.326959 -0.209 0.834738
Helpful.Family2 -0.106419 0.315159 -0.338 0.735613
Helpful.Family3 -0.231407 0.318939 -0.726 0.468113
Helpful.Family4 0.004491 0.350278 0.013 0.989772
Helpful.Family5 -0.175251 0.386057 -0.454 0.649864
Close.Friends1 0.580733 0.268130 2.166 0.030322 *
Close.Friends2 0.275533 0.265235 1.039 0.298887
Close.Friends3 0.226748 0.274193 0.827 0.408257
Close.Friends4 0.380765 0.334195 1.139 0.254558
Close.Friends5 -0.300196 0.379675 -0.791 0.429139
Community.TrustAgree 0.791737 0.469397 1.687 0.091659 .
Community.TrustDisagree 0.582946 0.452785 1.287 0.197931
Community.TrustNeutral 0.646587 0.456046 1.418 0.156246
Community.TrustStrongly agree 0.416849 0.542431 0.768 0.442201
Get.AlongAgree -0.519597 0.530808 -0.979 0.327640
Get.AlongDisagree -0.439306 0.516476 -0.851 0.395001
Get.AlongNeutral -0.584303 0.521787 -1.120 0.262794
Get.AlongStrongly agree -0.306626 0.589134 -0.520 0.602736
Health.InsuranceYes 0.626447 0.167038 3.750 0.000177 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2668.3 on 1926 degrees of freedom
Residual deviance: 2421.6 on 1874 degrees of freedom
AIC: 2527.6
Number of Fisher Scoring iterations: 4car::Anova(lr_mod)Analysis of Deviance Table (Type II tests)
Response: Heal.Professionals
LR Chisq Df Pr(>Chisq)
English.Speaking 36.073 3 7.226e-08 ***
Dental.Insurance 7.401 1 0.0065189 **
Religion 35.309 6 3.754e-06 ***
English.Difficulties 8.324 3 0.0397713 *
See.Family 4.035 5 0.5443406
Helpful.Friends 4.059 5 0.5408867
Close.Family 0.536 5 0.9907295
See.Friends 7.721 5 0.1722914
Helpful.Family 2.594 5 0.7623492
Close.Friends 9.996 5 0.0753429 .
Community.Trust 4.356 4 0.3599330
Get.Along 2.117 4 0.7142667
Health.Insurance 14.439 1 0.0001448 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1broom::tidy(lr_mod,exponentiate=T,conf.int=T)|> DT::datatable()Physical Check-up
#install.packages("randomForestSRC)
rfdata <- qol |>
select(`Physical Check-up`, Ethnicity, Age, Gender,Religion, `Full Time Employment`, Income_median, `English Speaking`, `English Difficulties`,`See Family`:`Community Trust`,`Health Insurance`,`Dental Insurance`,`Discrimination`) %>%
na.omit() |>
rename(Employment=`Full Time Employment`,
EnglishSpeak=`English Speaking`,
EnglishDiff=`English Difficulties`) |>
as.data.frame() |>
rename_with(make.names)
imbal <- ROSE::ROSE(Physical.Check.up~.,
data=rfdata,
seed=3)$data
VSURF(Physical.Check.up~.,imbal,na.action="na.omit",parallel=T,verbose=F)->vsurf.modWarning in VSURF.formula(Physical.Check.up ~ ., imbal, na.action = "na.omit", : VSURF with a formula-type call outputs selected variables
which are indices of the input matrix based on the formula:
you may reorder these to get indices of the original datavsurf.mod |> summary()
VSURF computation time: 32.2 secs
VSURF selected:
34 variables at thresholding step (in 7.9 secs)
17 variables at interpretation step (in 6.4 secs)
15 variables at prediction step (in 17.9 secs)
VSURF ran in parallel on a PSOCK cluster and used 15 cores names(rfdata[,-1])[vsurf.mod$varselect.pred] [1] "Dental.Insurance" "Age"
[3] "Health.Insurance" "Helpful.Family"
[5] "Religion" "Income_median"
[7] "EnglishDiff" "See.Family"
[9] "Close.Family" "Close.Friends"
[11] "EnglishSpeak" "See.Friends"
[13] "Community.Shares.Values" "Close.knit.Community"
[15] "Community.Trust" names(rfdata[,-1])[vsurf.mod$varselect.interp] [1] "Dental.Insurance" "Age"
[3] "Health.Insurance" "Ethnicity"
[5] "Helpful.Family" "Religion"
[7] "Income_median" "EnglishDiff"
[9] "See.Family" "Close.Family"
[11] "Close.Friends" "EnglishSpeak"
[13] "See.Friends" "Helpful.Friends"
[15] "Community.Shares.Values" "Close.knit.Community"
[17] "Community.Trust" plot(vsurf.mod)
vsurf.mod$mean.perf[1] 0.1037278Importance
vi<- data.frame(Variable=names(rfdata[,-1])[vsurf.mod$imp.mean.dec.ind],
Importance = vsurf.mod$imp.mean.dec,
sd_Importance = vsurf.mod$imp.sd.dec
)|>
mutate(fill = case_when(Variable=="Ethnicity"~"red",
.default="black"))
vi |> mutate(across(Importance:sd_Importance,~round(.x,5))) Variable Importance sd_Importance fill
1 Dental.Insurance 0.09161 0.00198 black
2 Age 0.07237 0.00111 black
3 Health.Insurance 0.05501 0.00159 black
4 Ethnicity 0.05173 0.00135 red
5 Helpful.Family 0.04063 0.00081 black
6 Religion 0.03983 0.00104 black
7 Income_median 0.03790 0.00174 black
8 EnglishDiff 0.03604 0.00103 black
9 See.Family 0.03404 0.00071 black
10 Close.Family 0.02988 0.00086 black
11 Close.Friends 0.02695 0.00078 black
12 EnglishSpeak 0.02679 0.00090 black
13 See.Friends 0.02582 0.00071 black
14 Helpful.Friends 0.02509 0.00055 black
15 Community.Shares.Values 0.02423 0.00088 black
16 Close.knit.Community 0.02380 0.00059 black
17 Community.Trust 0.02342 0.00073 black
18 Get.Along 0.02267 0.00098 black
19 Religious.Attendance 0.01972 0.00051 black
20 Helpful.Community 0.01717 0.00050 black
21 Gender 0.01704 0.00088 black
22 Religious.Importance 0.01701 0.00055 black
23 Expression 0.01411 0.00074 black
24 Successful.Family 0.01194 0.00057 black
25 Spend.Time.Together 0.01053 0.00038 black
26 Similar.Values 0.00962 0.00040 black
27 Discrimination 0.00956 0.00059 black
28 Family.Respect 0.00886 0.00037 black
29 Feel.Close 0.00863 0.00038 black
30 Employment 0.00834 0.00055 black
31 Loyalty 0.00778 0.00041 black
32 Family.Pride 0.00760 0.00030 black
33 Trust 0.00668 0.00038 black
34 Togetherness 0.00634 0.00029 blackimportance_plot <- ggplot(vi, aes(x = reorder(Variable, Importance), y = Importance, fill=fill))+
geom_bar(stat = "identity",alpha=0.4) +
geom_errorbar(aes(ymin=Importance-sd_Importance, ymax = Importance+sd_Importance))+
labs(title = "Variable Importance", x = "Variable", y = "Importance") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
scale_fill_manual(values=c("black","red"),
guide="none")
plot(importance_plot)
ggsave(filename = "VSURF_importance_PC.png", width=12, height=8,units="in")Logistic regression (Interpretation)
lr <- rfdata |> select(Physical.Check.up,names(rfdata[,-1])[vsurf.mod$varselect.pred])
lr_mod <- glm(Physical.Check.up~.,family=binomial,data=lr)
summary(lr_mod)
Call:
glm(formula = Physical.Check.up ~ ., family = binomial, data = lr)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.885273 0.643835 -4.481 7.42e-06 ***
Dental.InsuranceYes 0.618889 0.130749 4.733 2.21e-06 ***
Age 0.037943 0.004081 9.297 < 2e-16 ***
Health.InsuranceYes 1.235977 0.168603 7.331 2.29e-13 ***
Helpful.Family1 -0.031385 0.363957 -0.086 0.93128
Helpful.Family2 -0.395079 0.345046 -1.145 0.25221
Helpful.Family3 -0.151513 0.346810 -0.437 0.66220
Helpful.Family4 -0.361587 0.376575 -0.960 0.33695
Helpful.Family5 -0.312161 0.413601 -0.755 0.45041
ReligionBuddhist 0.211883 0.201085 1.054 0.29202
ReligionCatholic 0.152188 0.183801 0.828 0.40767
ReligionHindu -0.220516 0.187516 -1.176 0.23960
ReligionMuslim -0.052847 0.386113 -0.137 0.89113
ReligionOther -0.015476 0.452428 -0.034 0.97271
ReligionProtestant -0.277278 0.161935 -1.712 0.08684 .
Income_medianAbove 0.273903 0.122624 2.234 0.02550 *
EnglishDiffMuch -0.180242 0.189759 -0.950 0.34219
EnglishDiffNot much -0.534471 0.175164 -3.051 0.00228 **
EnglishDiffVery much -0.315604 0.168328 -1.875 0.06080 .
See.Family1 0.265111 0.355056 0.747 0.45526
See.Family2 -0.197328 0.290885 -0.678 0.49754
See.Family3 -0.105315 0.271954 -0.387 0.69857
See.Family4 -0.025685 0.292231 -0.088 0.92996
See.Family5 -0.033497 0.313288 -0.107 0.91485
Close.Family1 0.023836 0.269928 0.088 0.92964
Close.Family2 0.361724 0.260074 1.391 0.16427
Close.Family3 0.204018 0.267513 0.763 0.44568
Close.Family4 0.136660 0.324798 0.421 0.67394
Close.Family5 -0.238072 0.389460 -0.611 0.54101
Close.Friends1 0.246674 0.271003 0.910 0.36270
Close.Friends2 -0.016481 0.264998 -0.062 0.95041
Close.Friends3 0.015876 0.266783 0.060 0.95255
Close.Friends4 0.183421 0.323805 0.566 0.57108
Close.Friends5 0.031268 0.352851 0.089 0.92939
EnglishSpeakNot well 0.487198 0.266247 1.830 0.06727 .
EnglishSpeakVery well 0.920756 0.285594 3.224 0.00126 **
EnglishSpeakWell 0.760066 0.266735 2.850 0.00438 **
See.Friends1 0.273638 0.370295 0.739 0.45992
See.Friends2 0.193270 0.345511 0.559 0.57591
See.Friends3 0.234502 0.334260 0.702 0.48296
See.Friends4 0.207026 0.350242 0.591 0.55446
See.Friends5 0.485086 0.356099 1.362 0.17313
Community.Shares.ValuesAgree 0.472854 0.482594 0.980 0.32718
Community.Shares.ValuesDisagree 0.124787 0.472332 0.264 0.79163
Community.Shares.ValuesNeutral 0.362194 0.471921 0.767 0.44279
Community.Shares.ValuesStrongly agree 0.500342 0.551903 0.907 0.36463
Close.knit.CommunityAgree 0.148911 0.377084 0.395 0.69291
Close.knit.CommunityDisagree 0.375671 0.379135 0.991 0.32175
Close.knit.CommunityNeutral -0.046356 0.366989 -0.126 0.89948
Close.knit.CommunityStrongly agree 0.526118 0.440069 1.196 0.23188
Community.TrustAgree -0.477934 0.437691 -1.092 0.27486
Community.TrustDisagree -0.277239 0.430302 -0.644 0.51939
Community.TrustNeutral -0.494929 0.420999 -1.176 0.23975
Community.TrustStrongly agree -0.924469 0.519058 -1.781 0.07490 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2405.0 on 1917 degrees of freedom
Residual deviance: 2054.1 on 1864 degrees of freedom
AIC: 2162.1
Number of Fisher Scoring iterations: 4car::Anova(lr_mod)Analysis of Deviance Table (Type II tests)
Response: Physical.Check.up
LR Chisq Df Pr(>Chisq)
Dental.Insurance 22.270 1 2.369e-06 ***
Age 96.446 1 < 2.2e-16 ***
Health.Insurance 56.073 1 6.982e-14 ***
Helpful.Family 4.924 5 0.425225
Religion 11.242 6 0.081191 .
Income_median 4.974 1 0.025735 *
EnglishDiff 11.635 3 0.008744 **
See.Family 2.778 5 0.734096
Close.Family 6.128 5 0.293966
Close.Friends 2.501 5 0.776273
EnglishSpeak 11.981 3 0.007449 **
See.Friends 3.463 5 0.628955
Community.Shares.Values 3.037 4 0.551656
Close.knit.Community 8.204 4 0.084401 .
Community.Trust 4.263 4 0.371563
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1broom::tidy(lr_mod,exponentiate=T,conf.int=T)|> DT::datatable()Dental Check-up
rfdata <- qol |> select(`Dentist Check-up`, Ethnicity, Age, Gender,Religion, `Full Time Employment`, Income_median, `English Speaking`, `English Difficulties`,`See Family`:`Community Trust`,`Health Insurance`,`Dental Insurance`,`Discrimination`) |>
na.omit() |>
as.data.frame() |>
rename_with(make.names)
imbal <- ROSE::ROSE(Dentist.Check.up~.,
data=rfdata,
seed=3)$data
VSURF(Dentist.Check.up~.,imbal,na.action="na.omit",parallel=T,verbose=F)->vsurf.modWarning in VSURF.formula(Dentist.Check.up ~ ., imbal, na.action = "na.omit", : VSURF with a formula-type call outputs selected variables
which are indices of the input matrix based on the formula:
you may reorder these to get indices of the original datavsurf.mod |> summary()
VSURF computation time: 23.4 secs
VSURF selected:
34 variables at thresholding step (in 8 secs)
18 variables at interpretation step (in 7.1 secs)
7 variables at prediction step (in 8.3 secs)
VSURF ran in parallel on a PSOCK cluster and used 15 cores names(rfdata[,-1])[vsurf.mod$varselect.pred][1] "Dental.Insurance" "Ethnicity"
[3] "English.Speaking" "Health.Insurance"
[5] "Community.Trust" "Community.Shares.Values"
[7] "Close.knit.Community" names(rfdata[,-1])[vsurf.mod$varselect.interp] [1] "Dental.Insurance" "Ethnicity"
[3] "Religion" "Age"
[5] "See.Friends" "See.Family"
[7] "Close.Friends" "Income_median"
[9] "Helpful.Family" "Close.Family"
[11] "English.Difficulties" "Helpful.Friends"
[13] "Religious.Importance" "English.Speaking"
[15] "Health.Insurance" "Community.Trust"
[17] "Community.Shares.Values" "Close.knit.Community" plot(vsurf.mod)
vsurf.mod$mean.perf[1] 0.111906Importance
vi<- data.frame(Variable=names(rfdata[,-1])[vsurf.mod$imp.mean.dec.ind],
Importance = vsurf.mod$imp.mean.dec,
sd_Importance = vsurf.mod$imp.sd.dec
)|>
mutate(fill = case_when(Variable=="Ethnicity"~"red",
.default="black"))
vi |> mutate(across(Importance:sd_Importance,~round(.x,5))) Variable Importance sd_Importance fill
1 Dental.Insurance 0.11737 0.00143 black
2 Ethnicity 0.06255 0.00156 red
3 Religion 0.05053 0.00114 black
4 Age 0.04821 0.00137 black
5 See.Friends 0.04104 0.00088 black
6 See.Family 0.03811 0.00089 black
7 Close.Friends 0.03651 0.00065 black
8 Income_median 0.03501 0.00190 black
9 Helpful.Family 0.03278 0.00070 black
10 Close.Family 0.03266 0.00104 black
11 English.Difficulties 0.02789 0.00074 black
12 Helpful.Friends 0.02611 0.00062 black
13 Religious.Importance 0.02575 0.00072 black
14 English.Speaking 0.02446 0.00074 black
15 Health.Insurance 0.02414 0.00108 black
16 Community.Trust 0.02173 0.00067 black
17 Community.Shares.Values 0.01749 0.00037 black
18 Close.knit.Community 0.01743 0.00059 black
19 Get.Along 0.01634 0.00051 black
20 Religious.Attendance 0.01588 0.00053 black
21 Helpful.Community 0.01526 0.00050 black
22 Gender 0.01339 0.00067 black
23 Spend.Time.Together 0.01195 0.00041 black
24 Feel.Close 0.01184 0.00050 black
25 Expression 0.00993 0.00036 black
26 Similar.Values 0.00980 0.00044 black
27 Successful.Family 0.00921 0.00044 black
28 Discrimination 0.00783 0.00038 black
29 Full.Time.Employment 0.00736 0.00050 black
30 Trust 0.00730 0.00033 black
31 Family.Respect 0.00679 0.00034 black
32 Family.Pride 0.00640 0.00026 black
33 Togetherness 0.00596 0.00035 black
34 Loyalty 0.00520 0.00025 blackimportance_plot <- ggplot(vi, aes(x = reorder(Variable, Importance), y = Importance, fill=fill))+
geom_bar(stat = "identity",alpha=0.4) +
geom_errorbar(aes(ymin=Importance-sd_Importance, ymax = Importance+sd_Importance))+
labs(title = "Variable Importance", x = "Variable", y = "Importance") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
scale_fill_manual(values=c("black","red"),
guide="none")
plot(importance_plot)
ggsave(filename = "VSURF_importance_Dc.png", width=12, height=8,units="in")Logistic regression (Interpretation)
lr <- rfdata |> select(Dentist.Check.up,names(rfdata[,-1])[vsurf.mod$varselect.pred])
lr_mod <- glm(Dentist.Check.up~.,family=binomial,data=lr)
summary(lr_mod)
Call:
glm(formula = Dentist.Check.up ~ ., family = binomial, data = lr)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.69290 0.44772 -3.781 0.000156 ***
Dental.InsuranceYes 1.38576 0.11872 11.672 < 2e-16 ***
EthnicityAsian Indian -1.32396 0.16662 -7.946 1.92e-15 ***
EthnicityFilipino -0.20775 0.21632 -0.960 0.336859
EthnicityKorean -0.38852 0.16064 -2.419 0.015583 *
EthnicityOther -0.42908 0.25378 -1.691 0.090879 .
EthnicityVietnamese -0.08924 0.16700 -0.534 0.593081
English.SpeakingNot well 0.86831 0.27087 3.206 0.001347 **
English.SpeakingVery well 1.30769 0.27446 4.765 1.89e-06 ***
English.SpeakingWell 0.94587 0.27070 3.494 0.000476 ***
Health.InsuranceYes 0.47126 0.16708 2.821 0.004794 **
Community.TrustAgree 0.18361 0.41844 0.439 0.660809
Community.TrustDisagree -0.13516 0.41120 -0.329 0.742389
Community.TrustNeutral 0.13742 0.40449 0.340 0.734061
Community.TrustStrongly agree -0.22904 0.50261 -0.456 0.648604
Community.Shares.ValuesAgree 0.39102 0.45290 0.863 0.387934
Community.Shares.ValuesDisagree 0.21608 0.44427 0.486 0.626700
Community.Shares.ValuesNeutral 0.27058 0.44223 0.612 0.540631
Community.Shares.ValuesStrongly agree -0.02971 0.52298 -0.057 0.954692
Close.knit.CommunityAgree -0.24180 0.37320 -0.648 0.517052
Close.knit.CommunityDisagree 0.01365 0.37557 0.036 0.971005
Close.knit.CommunityNeutral -0.19066 0.36411 -0.524 0.600528
Close.knit.CommunityStrongly agree 0.67349 0.43246 1.557 0.119386
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2593.0 on 1914 degrees of freedom
Residual deviance: 2204.4 on 1892 degrees of freedom
AIC: 2250.4
Number of Fisher Scoring iterations: 4car::Anova(lr_mod)Analysis of Deviance Table (Type II tests)
Response: Dentist.Check.up
LR Chisq Df Pr(>Chisq)
Dental.Insurance 141.886 1 < 2.2e-16 ***
Ethnicity 83.650 5 < 2.2e-16 ***
English.Speaking 27.434 3 4.775e-06 ***
Health.Insurance 8.088 1 0.004455 **
Community.Trust 3.844 4 0.427558
Community.Shares.Values 2.918 4 0.571617
Close.knit.Community 14.337 4 0.006295 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1broom::tidy(lr_mod,exponentiate=T,conf.int=T)|> DT::datatable()Folkmedicine
rfdata <- qol |> select(`Folkmedicine`, Ethnicity, Age, Gender,Religion, `Full Time Employment`, Income_median, `English Speaking`, `English Difficulties`,`See Family`:`Community Trust`,`Health Insurance`,`Dental Insurance`,`Discrimination`) |>
na.omit() |>
as.data.frame() |>
rename_with(make.names)
imbal <- ROSE::ROSE(Folkmedicine~.,
data=rfdata,
seed=3)$data
VSURF(Folkmedicine~.,imbal,na.action="na.omit",parallel=T,verbose=F)->vsurf.modWarning in VSURF.formula(Folkmedicine ~ ., imbal, na.action = "na.omit", : VSURF with a formula-type call outputs selected variables
which are indices of the input matrix based on the formula:
you may reorder these to get indices of the original datavsurf.mod |> summary()
VSURF computation time: 52.4 secs
VSURF selected:
34 variables at thresholding step (in 7.5 secs)
28 variables at interpretation step (in 6.8 secs)
15 variables at prediction step (in 38.1 secs)
VSURF ran in parallel on a PSOCK cluster and used 15 cores names(rfdata[,-1])[vsurf.mod$varselect.pred] [1] "Ethnicity" "Age" "See.Friends"
[4] "Religion" "English.Speaking" "See.Family"
[7] "Helpful.Family" "Full.Time.Employment" "Close.Family"
[10] "Religious.Attendance" "Helpful.Friends" "English.Difficulties"
[13] "Close.Friends" "Get.Along" "Religious.Importance"names(rfdata[,-1])[vsurf.mod$varselect.interp] [1] "Ethnicity" "Age"
[3] "See.Friends" "Religion"
[5] "English.Speaking" "See.Family"
[7] "Helpful.Family" "Full.Time.Employment"
[9] "Close.Family" "Religious.Attendance"
[11] "Helpful.Friends" "English.Difficulties"
[13] "Close.Friends" "Get.Along"
[15] "Religious.Importance" "Helpful.Community"
[17] "Close.knit.Community" "Community.Trust"
[19] "Community.Shares.Values" "Discrimination"
[21] "Feel.Close" "Similar.Values"
[23] "Expression" "Gender"
[25] "Spend.Time.Together" "Togetherness"
[27] "Trust" "Successful.Family" plot(vsurf.mod)
vsurf.mod$mean.perf[1] 0.04842022Importance
vi<- data.frame(Variable=names(rfdata[,-1])[vsurf.mod$imp.mean.dec.ind],
Importance = vsurf.mod$imp.mean.dec,
sd_Importance = vsurf.mod$imp.sd.dec
)|>
mutate(fill = case_when(Variable=="Ethnicity"~"red",
.default="black"))
vi |> mutate(across(Importance:sd_Importance,~round(.x,5))) Variable Importance sd_Importance fill
1 Ethnicity 0.10602 0.00196 red
2 Age 0.09538 0.00156 black
3 See.Friends 0.06796 0.00142 black
4 Religion 0.06783 0.00227 black
5 English.Speaking 0.06154 0.00200 black
6 See.Family 0.05878 0.00101 black
7 Helpful.Family 0.05443 0.00122 black
8 Full.Time.Employment 0.05117 0.00174 black
9 Close.Family 0.04806 0.00086 black
10 Religious.Attendance 0.04505 0.00146 black
11 Helpful.Friends 0.04279 0.00131 black
12 English.Difficulties 0.04279 0.00143 black
13 Close.Friends 0.04039 0.00088 black
14 Get.Along 0.03517 0.00120 black
15 Religious.Importance 0.03280 0.00103 black
16 Helpful.Community 0.03131 0.00130 black
17 Close.knit.Community 0.02980 0.00095 black
18 Community.Trust 0.02932 0.00113 black
19 Community.Shares.Values 0.02833 0.00076 black
20 Discrimination 0.02112 0.00116 black
21 Feel.Close 0.01956 0.00080 black
22 Similar.Values 0.01921 0.00071 black
23 Expression 0.01755 0.00067 black
24 Gender 0.01604 0.00070 black
25 Spend.Time.Together 0.01602 0.00085 black
26 Togetherness 0.01579 0.00057 black
27 Trust 0.01523 0.00060 black
28 Successful.Family 0.01447 0.00041 black
29 Family.Pride 0.01283 0.00052 black
30 Family.Respect 0.01271 0.00053 black
31 Income_median 0.01160 0.00049 black
32 Loyalty 0.01095 0.00057 black
33 Dental.Insurance 0.01084 0.00055 black
34 Health.Insurance 0.00472 0.00026 blackimportance_plot <- ggplot(vi, aes(x = reorder(Variable, Importance), y = Importance, fill=fill))+
geom_bar(stat = "identity",alpha=0.4) +
geom_errorbar(aes(ymin=Importance-sd_Importance, ymax = Importance+sd_Importance))+
labs(title = "Variable Importance", x = "Variable", y = "Importance") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
scale_fill_manual(values=c("black","red"),
guide="none")
plot(importance_plot)
ggsave(filename = "VSURF_importance_Alt.png", width=12, height=8,units="in")Logistic regression (Interpretation)
lr <- rfdata |> select(Folkmedicine,names(rfdata[,-1])[vsurf.mod$varselect.pred])
lr_mod <- glm(Folkmedicine~.,family=binomial,data=lr)
summary(lr_mod)
Call:
glm(formula = Folkmedicine ~ ., family = binomial, data = lr)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.016335 0.934940 -3.226 0.00125
EthnicityAsian Indian -0.030272 0.473625 -0.064 0.94904
EthnicityFilipino -0.635937 0.356061 -1.786 0.07409
EthnicityKorean 0.141827 0.214351 0.662 0.50819
EthnicityOther -0.581345 0.395935 -1.468 0.14203
EthnicityVietnamese -1.220005 0.290934 -4.193 2.75e-05
Age 0.022399 0.004758 4.708 2.50e-06
See.Friends1 -0.409254 0.513238 -0.797 0.42522
See.Friends2 0.035686 0.468279 0.076 0.93926
See.Friends3 -0.567571 0.466071 -1.218 0.22331
See.Friends4 -0.049861 0.482450 -0.103 0.91769
See.Friends5 -0.098293 0.493940 -0.199 0.84226
ReligionBuddhist 0.347368 0.315124 1.102 0.27032
ReligionCatholic -0.033902 0.358719 -0.095 0.92471
ReligionHindu -0.465896 0.544998 -0.855 0.39263
ReligionMuslim -2.281513 1.126714 -2.025 0.04287
ReligionOther 0.288843 0.619801 0.466 0.64120
ReligionProtestant 0.044756 0.321309 0.139 0.88922
English.SpeakingNot well -0.235820 0.311594 -0.757 0.44916
English.SpeakingVery well -0.695889 0.349007 -1.994 0.04616
English.SpeakingWell -0.133941 0.314184 -0.426 0.66988
See.Family1 0.454397 0.475916 0.955 0.33969
See.Family2 0.340928 0.421214 0.809 0.41829
See.Family3 0.679576 0.397195 1.711 0.08709
See.Family4 0.779675 0.421481 1.850 0.06434
See.Family5 0.523445 0.455067 1.150 0.25004
Helpful.Family1 -0.046634 0.486931 -0.096 0.92370
Helpful.Family2 -0.405006 0.473538 -0.855 0.39240
Helpful.Family3 -0.694874 0.486922 -1.427 0.15356
Helpful.Family4 -0.458485 0.527260 -0.870 0.38454
Helpful.Family5 -0.574532 0.586975 -0.979 0.32768
Full.Time.EmploymentEmployed full time -0.211554 0.154986 -1.365 0.17226
Close.Family1 0.471797 0.402300 1.173 0.24090
Close.Family2 0.437042 0.393382 1.111 0.26657
Close.Family3 0.251498 0.408651 0.615 0.53827
Close.Family4 0.271550 0.472489 0.575 0.56548
Close.Family5 0.020205 0.584695 0.035 0.97243
Religious.AttendanceNever 0.040029 0.313644 0.128 0.89844
Religious.AttendanceOnce or twice a month 0.258662 0.286676 0.902 0.36691
Religious.AttendanceSeldom 0.228633 0.306964 0.745 0.45638
Helpful.Friends1 0.786293 0.477640 1.646 0.09972
Helpful.Friends2 0.690687 0.474400 1.456 0.14542
Helpful.Friends3 0.450932 0.489155 0.922 0.35660
Helpful.Friends4 0.388475 0.544055 0.714 0.47520
Helpful.Friends5 0.396188 0.600617 0.660 0.50949
English.DifficultiesMuch -0.151615 0.243073 -0.624 0.53280
English.DifficultiesNot much -0.114987 0.223525 -0.514 0.60696
English.DifficultiesVery much -0.279831 0.241611 -1.158 0.24679
Close.Friends1 -0.221262 0.410049 -0.540 0.58947
Close.Friends2 0.137090 0.411928 0.333 0.73929
Close.Friends3 0.401494 0.422132 0.951 0.34155
Close.Friends4 0.388513 0.494921 0.785 0.43245
Close.Friends5 0.349774 0.576811 0.606 0.54425
Get.AlongAgree -0.283180 0.514286 -0.551 0.58189
Get.AlongDisagree -0.229422 0.549949 -0.417 0.67656
Get.AlongNeutral -0.000129 0.510670 0.000 0.99980
Get.AlongStrongly agree -0.342164 0.557705 -0.614 0.53953
Religious.ImportanceNot very important 0.055863 0.300491 0.186 0.85252
Religious.ImportanceSomewhat important -0.088913 0.340581 -0.261 0.79404
Religious.ImportanceVery important 0.157524 0.362294 0.435 0.66371
(Intercept) **
EthnicityAsian Indian
EthnicityFilipino .
EthnicityKorean
EthnicityOther
EthnicityVietnamese ***
Age ***
See.Friends1
See.Friends2
See.Friends3
See.Friends4
See.Friends5
ReligionBuddhist
ReligionCatholic
ReligionHindu
ReligionMuslim *
ReligionOther
ReligionProtestant
English.SpeakingNot well
English.SpeakingVery well *
English.SpeakingWell
See.Family1
See.Family2
See.Family3 .
See.Family4 .
See.Family5
Helpful.Family1
Helpful.Family2
Helpful.Family3
Helpful.Family4
Helpful.Family5
Full.Time.EmploymentEmployed full time
Close.Family1
Close.Family2
Close.Family3
Close.Family4
Close.Family5
Religious.AttendanceNever
Religious.AttendanceOnce or twice a month
Religious.AttendanceSeldom
Helpful.Friends1 .
Helpful.Friends2
Helpful.Friends3
Helpful.Friends4
Helpful.Friends5
English.DifficultiesMuch
English.DifficultiesNot much
English.DifficultiesVery much
Close.Friends1
Close.Friends2
Close.Friends3
Close.Friends4
Close.Friends5
Get.AlongAgree
Get.AlongDisagree
Get.AlongNeutral
Get.AlongStrongly agree
Religious.ImportanceNot very important
Religious.ImportanceSomewhat important
Religious.ImportanceVery important
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1505.5 on 1898 degrees of freedom
Residual deviance: 1337.0 on 1839 degrees of freedom
AIC: 1457
Number of Fisher Scoring iterations: 6car::Anova(lr_mod)Analysis of Deviance Table (Type II tests)
Response: Folkmedicine
LR Chisq Df Pr(>Chisq)
Ethnicity 27.3067 5 4.971e-05 ***
Age 22.1591 1 2.510e-06 ***
See.Friends 10.3396 5 0.06617 .
Religion 10.0898 6 0.12092
English.Speaking 7.5875 3 0.05535 .
See.Family 5.8019 5 0.32597
Helpful.Family 5.8421 5 0.32189
Full.Time.Employment 1.8742 1 0.17100
Close.Family 2.5272 5 0.77239
Religious.Attendance 1.3966 3 0.70633
Helpful.Friends 3.9530 5 0.55621
English.Difficulties 1.3945 3 0.70682
Close.Friends 3.8251 5 0.57486
Get.Along 3.6842 4 0.45043
Religious.Importance 1.5161 3 0.67857
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1broom::tidy(lr_mod,exponentiate=T,conf.int=T)|> DT::datatable()